Multi-Variate Time Series Forecasting on Variable Subsets

Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran

Abstract:

We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast
(VSF), where only a small subset of the variables is available during
inference. Variables are absent during inference because of longterm data loss (eg. sensor failures) or high→low-resource domain
shift between train / test. To the best of our knowledge, robustness
of MTSF models in presence of such failures, has not been studied
in the literature. Through extensive evaluation, we first show that
the performance of state of the art methods degrade significantly in
the VSF setting. We propose a non-parametric, wrapper technique
that can be applied on top any existing forecast models. Through
systematic experiments across 4 datasets and 5 forecast models,
we show that our technique is able to recover close to 95% performance of the models even when only 15% of the original variables
are present.